Artificial Intelligence for Oil and Gas Using Python equips participants with the theoretical and practical knowledge needed to apply Machine Learning and Deep Learning concepts to the fields of geosciences and engineering.
Machine Learning is a subfield of Artificial Intelligence, which is based on trying to imitate the actions of human beings through the training of algorithms. This branch of Data Science is booming in various areas of geosciences, including electrical well log interpretation, reservoir characterization, seismic interpretation, identification of areas with high mining potential, among others.
Deep Learning focuses on the use of neural networks and applying the "back-propagation" method to adjust errors resulting from different iterations, and, when enough data is available, to obtain results far superior to those obtained by "classical" learning algorithms in Machine Learning.
This course will allow participants to apply the knowledge and algorithms learned immediately, both in their research, as well as in their professional career.
Artificial Intelligence for Oil and Gas Using Python includes four self-paced independent study modules, along with three interactive working sessions with the instructor.
The format allows participants to work at their own pace and to reach out to the instructor for support throughout the week and during the live sessions.
Course modules will be available for viewing on the website from August 1- 31, 2022. Live sessions take place over three Saturdays, on August 6, 13 and 27, from 8 a.m. – 12 p.m. CDT/COT (GMT -5).
Course modules will be delivered in English, and the instructor will conduct working sessions in English and Spanish, accommodating participants’ preference.
Session 1: Python Basics
- Types of Data and Data Management
- Definition and Execution of Functions
- Primary Python Libraries:
- Exploratory Data Analysis
- Plot a geochemical dataset
- Visualize, organize, and analyze an oil/gas production dataset
Session 2: Applied Python in Geology and Geophysics
Wavelet (Ricker) in time and frequency
- Well logs
- Display Geospatial Data (Mineral information)
- Seismic volume load
- Post-Stack seismic attributes calculation
- Generate a Ormsby, and Butterworth wavelet (Time and Domain)
- Calculate Coherence Attributes using Python
Session 3: Machine Learning
- Supervised vs. Unsupervised
- Logistic Regression
- Dimension Reduction
- Principal Component Analysis (PCA)
- Facies Classification of Well Logs
- Use Regression Methods to forecast production in an oil well
Session 4: Deep Learning
- Programming a Neural Network (step by step)
- Application of Neural Networks for facies prediction
- Neural Networks for:
- Missing Well logs
- Production forecast of an oil well
- Use CNN for object detection in seismic images
- Use CNN to predict salt bodies in a seismic dataset
- Classify 3D Seismic Facies using Deep Neural Networks
Working Session Schedule
Python Basics & Applied Python